Defining and measuring food insecurity at the household level is critical for policymakers,
aid agencies, and international organizations. Food insecurity indicators, such as the Food …

Estimating how many people are food insecure and where they are is of fundamental
importance for governments and humanitarian organizations to make informed and timely …

Mounting concerns over food insecurity have emerged as a key agenda in many recent
global development dialogues, on accounts of observed and expected health outcomes …

Food insecurity early warning can provide time to mitigate unfolding crises; however,
drought remains a large source of uncertainty. The challenge is to filter unclear or conflicting …

After many years of decline, hunger in Africa is growing again. This represents a global
societal issue that all disciplines concerned with data analysis are facing. The rapid and …

The complex and uncertain environment of the humanitarian response to crises can lead to
data bias, which can affect decision-making. Evidence of data bias in crisis information …

Anticipating food crisis outbreaks is crucial to efficiently allocate emergency relief and
reduce human suffering. However, existing predictive models rely on risk measures that are …

Food insecurity is a growing concern due to man-made conflicts, climate change, and
economic downturns. Forecasting the state of food insecurity is essential to be able to trigger …

Advances in remote sensing and machine learning enable increasingly accurate,
inexpensive, and timely estimation of poverty and malnutrition indicators to guide …

Abstract Machine learning (ML) holds potential to predict hunger crises before they occur.
Yet, ML models embed crucial choices that affect their utility. We develop a prototype model …